摘要
表面缺陷检测是产品质量检测的关键环节,近年来随着深度学习技术的迅速发展,金属材料表面缺陷检测技术大幅提升。对近几年基于深度学习的金属材料表面缺陷检测方法进行了梳理和分析,并从监督方法、无监督方法以及弱监督方法 3个方面对比论述了近年来的研究现状及应用效果。最后系统总结了金属材料表面缺陷检测中的关键问题及解决方法。结合工业需求,对表面缺陷检测的进一步发展进行了思考与展望。
Surface defect detection is a key part of product quality inspection,and with the rapid development of deep learning technology in recent years,the surface defect detection technology of metal materials has been greatly improved.This paper compares and analyzes the surface defect detection methods of metal materials based on deep learning in recent years,and discusses the status of research and application effects in recent years from three aspects:supervised methods,unsupervised methods,and weakly supervised methods.Finally,the key problems and solutions in the detection of surface defects in metal materials are systematically summarized.The further development of surface defect detection is considered and foreseen in the light of industrial needs.
作者
李宗祐
高春艳
吕晓玲
张明路
LI Zongyou;GAO Chunyan;LV Xiaoling;ZHANG Minglu(School of Mechanical Engineering,Hebei University of Technology,Tianjin 300401,CHN)
出处
《制造技术与机床》
北大核心
2023年第6期61-67,共7页
Manufacturing Technology & Machine Tool
基金
国家自然科学基金重点项目(U1913211)。
关键词
表面缺陷检测
深度学习
卷积神经网络
金属材料
图像处理
surface defect detection
deep learning
convolutional neural network
metal materials
image processing
